1. 用于分类模型：

import numpy as np
import scipy.misc
import cv2
import os

# DF1
path = "/home/pi/工作/predict1/"
npy_list = os.listdir(path)
save_path = "/home/pi/predict1_img/"
if not os.path.exists(save_path):
 os.mkdir(save_path)

for i in range(0, len(npy_list)):
 print(i)
 print(npy_list[i])
 npy_full_path = os.path.join(path, npy_list[i])
 img = np.load(npy_full_path) # load进来

 save_full_path = os.path.join(save_path, npy_list[i][:-4])
 scipy.misc.imsave(save_full_path, img) # 保存

2. 用于分割模型

"""
将数据集随机分成训练集、测试集
传入参数：
ratio = 0.7 # 训练样本比例
path = "/home/pi/20190701_0705" # 数据路径
new_path = "/home/pi/20190701_0705_new2" # 保存路径
使用方法：
temp = Generate_Train_and_Test(path, new_path, ratio)
temp.splict_data()
"""
import random
import os
import cv2

def makeDir(path):
 try:
  if not os.path.exists(path):
   if not os.path.isfile(path):
    # os.mkdir(path)
    os.makedirs(path)
    return 0
  else:
   return 1
 except Exception as e:
  print(str(e))
  return -2

class Generate_Train_and_Test:

 def __init__(self, path, new_path, ratio):
  if not os.path.exists(new_path):
   makeDir(new_path)
  self.path = path
  self.new_path = new_path
  self.ratio = ratio
  self.train_sample_path = os.path.join(new_path, "train")
  self.test_sample_path = os.path.join(new_path, "test")

  makeDir(self.train_sample_path)
  makeDir(self.test_sample_path)

 def splict_data(self):
  class_names = os.listdir(self.path) # 类别：bg and ng10
  for name in class_names:
   print("process class name=%s" % name)
   tmp_class_name = os.path.join(self.path, name)
   save_train_class_name = os.path.join(self.train_sample_path, name)
   save_test_class_name = os.path.join(self.test_sample_path, name)
   makeDir(save_train_class_name)
   makeDir(save_test_class_name)
   if os.path.isdir(tmp_class_name):
    image_names = os.listdir(tmp_class_name) # 其中一个类别的所有图像
    image_names = [f for f in image_names if not f.endswith('_mask.png')]
    total = len(image_names)

    # 1, 打乱当前类中所有图像
    random.shuffle(image_names)

    # 2, 从当前类(ng)中，取前面的图像作为train data
    train_temp = int(self.ratio * total) # 打乱后，取前面作为train_data
    for i in range(0, train_temp):
     print(i, image_names[i])
     temp_img_name = os.path.join(tmp_class_name, image_names[i])
     train_image = cv2.imread(temp_img_name)
     temp_label_name = os.path.join(tmp_class_name, image_names[i][:-4] + '_mask.png')
     train_label = cv2.imread(temp_label_name)

     save_train_img_name = os.path.join(save_train_class_name, image_names[i])
     cv2.imwrite(save_train_img_name, train_image)

     save_train_label_name = os.path.join(save_train_class_name, image_names[i][:-4] + '_mask.png')
     cv2.imwrite(save_train_label_name, train_label)

    # 3, 从当前类(bg)中，取后面的图像作为test data
    for i in range(train_temp, total):
     print(i, image_names[i])
     test_img_name = os.path.join(tmp_class_name, image_names[i])
     test_image = cv2.imread(test_img_name)
     test_label_name = os.path.join(tmp_class_name, image_names[i][:-4] + '_mask.png')
     test_label = cv2.imread(test_label_name)

     save_test_img_name = os.path.join(save_test_class_name, image_names[i])
     cv2.imwrite(save_test_img_name, test_image)

     save_test_label_name = os.path.join(save_test_class_name, image_names[i][:-4] + '_mask.png')
     cv2.imwrite(save_test_label_name, test_label)

ratio = 0.7 # 训练样本比例
path = "/home/pi/工作/20190712_splict" # 数据路径
new_path = "/home/pi/工作/20190712_splict_new3" # 保存路径

temp = Generate_Train_and_Test(path, new_path, ratio)
temp.splict_data()

补充知识：python把由图片组成的文件夹转换为.npy文件

由于深度神经网络的需要，我要将一个里面全是.png格式的图片的文件夹转换为一个.npy文件，即将一个图片文件夹转换成一个.npy文件。

具体思路为：

若已知文件夹中图片数量，可生成一个三维数组，第一维表示图片数量，后两维表示一张图片的尺寸；

利用np.save()函数将生成的三维数组保存成一个.npy文件

import numpy as np
import imageio
import os
os.chdir('E:/RegistrationCode/papercode/datasets/mri_2d_test')  #切换python工作路径到你要操作的图片文件夹，mri_2d_test为我的图片文件夹
a=np.ones((190,192,160)) #利用np.ones()函数生成一个三维数组，当然也可用np.zeros，此数组的每个元素a[i]保存一张图片
i=0
for filename in os.listdir(r"E:/RegistrationCode/papercode/datasets/mri_2d_test"): #使用os.listdir()获取该文件夹下每一张图片的名字
 im=imageio.imread(filename)
 a[i]=im
 i=i+1
 if(i==190): #190为文件夹中的图片数量
 break
np.save('你要保存的.npy文件所在路径及名字',a)

以上这篇使用npy转image图像并保存的实例就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持python博客。